Troy's Scratchpad

March 15, 2014

Does the Shindell (2014) apparent TCR estimate bias apply to the real world?

Filed under: Uncategorized — troyca @ 9:35 am

There has been some considerable discussion of Shindell (2014) and the suggestion that usual estimates of TCR (which assume roughly equal efficacies for different forcings), such as Otto et al, (2013), might be underestimating TCR with the traditional method.  A few example discussing Shindell (2014) are at Skeptical Science, And Then There’s Physics, and Climate Audit.  SkS’s Dana went so far as to say the paper “demolishes” Lewis and Crok’s report at James Annan’s blog, but JA responds quite skeptically of the Shindell (2014) results.

On the face of it, the argument is fairly simple and intuitive (so buyer beware!): since the cooling effect of aerosols generally occur in the Northern Hemisphere where there is greater land mass and thus lower effective heat capacity, these forcings will disproportionally affect the global temperature relative to the forcing of well-mixed greenhouse gases, which acts globally.  Since Watt per Watt these cooling forcings will give you more bang for your buck, an estimate of TCR using only the globally averaged forcing and global temperature could be biased low.  Shindell (2014) therefore tries to find the average “enhancement” of aerosol+O3 forcings, E, through GCMs, and uses the following to calculate TCR from global quantities:

TCR = F_2xCO2 x (dT_obs / (F_ghg + E x (F_aerosols + F_Ozone + F_LU)))

However, there are reasons to be skeptical of this result as well.  For one, there *have* been studies that specifically looked at the rate of warming and certainly don’t assume homogenous forcings, such as Gillett et al (2012), which find low TCR estimates consistent with Otto et al., (2013).  Furthermore, Shindell (2014) does not seem to consider the ratio of observed warming in the NH vs. SH. ..using Cowtan and Way (2013) with HadCRUT4 kriging, from the base period (1860-1879) through the end of the historical simulation time period (1996-2005), the ratio of NH warming to SH warming is 1.48.  Obviously, if there was a large cooling effect from aerosols concentrated primarily in the NH (due to a large enhancement of the aerosol effect), we would expect to see more warming in the SH than the NH!  Third, there did not seem to be any tests run on the actual historical simulations from models, which would tell us how well the Shindell (2014) method performs relative to the “simple” E=1.0 method (e.g. Otto et al).  These last tests should be easy to pass, since the value of “E” would be calculated from the same model that the tests are run on (unlike the real world, where we don’t know the “true” value of E).

The first table simply shows the forcings and temperature changes using the same models as S14, as much of this information is available in the supplement.  These tests will be based on the difference between the base period (1860-1879) and the end of the historical simulation (1996-2005) using the historical runs.

TABLE 1. Temperature + Forcing from historical simulations and Aero/O3 Enhancement (from Shindell 2014)











































One thing that has been nagging me about this is that natural forcings are not included in the TCR equation above.  I am not sure if the slightly positive solar influence is balanced out by the slightly negative volcanic influence in models, or what, but S14 does not include estimates of these natural forcings in the models so I have not included them in the tests either. And here are the results of the actual tests using the above numbers: 

TABLE 2. Estimate of TCR using Simple (E=1.0) estimate vs. Shindell (2014) methods, along with NH/SH warming ratios


Simple Estimate (K)

Shindell Estimate(K)

TCR Actual (K)



NH/SH Hist


















































Observed (CW13)




Assuming I have not messed something up here, these results appear to be very concerning for Shindell (2014).  For example, IPSL-CM5-LR, the model from S14 with the largest “enhancement” at E=2.43, would be expected to yield a major underestimate of TCR using the simple method.  Instead, the simple estimate only underestimates TCR by 6%, whereas applying the S14 “correction” makes things far worse, yielding a 40% overestimate of TCR!  In fact, in 4 of the 6 models, the Shindell method overestimates the TCR by > 30%.  On the other hand, the “simple” method only underestimates TCR by > 30% in 1 of the 6 cases.  

Perhaps even more concerning, however, is the specific model ensembles for which the Shindell (2014) method largely overestimates the TCR.  Given the observed NH/SH warming ratio of 1.48, the two models that are most realistic in this regard are IPSL-CM5-LR (1.49) and the average of MRI, NOR, and MIROC (1.58).   Since the argument from Shindell (2014) essentially hinges on the NH being disproportionately cooled by aerosols relative to the SH, these are the most directly relevant.  And yet, using the “simple” method in these cases produces underestimates of 6% and 2%, which would hardly change the results of a paper like Otto et al, 2013.  On the other hand, the Shindell (2014) “correction” causes overestimates of 40% and 34%  (e.g. a shift from a 1.3K “most likely” value from Otto to the 1.7K reported by S14).  If we look at the model that the “simple” method largely underestimates, GFDL-CM3, we see that the 0.84 NH/SH in that model is the farthest away from the one we’ve observed in the real world, suggesting it is likely the least relevant. 

In fact, I would argue that the amplification of the NH/SH ratio in the historicalGHG simulation relative to the historical simulation for a model could be used to better estimate the TCR “bias” calculated using that model.  This is because the difference in the NH/SH ratio in the historicalGHG simulation and that of the historical simulation implicitly combines the actual aerosol forcing and the “enhancement” of this forcing (rather than trying to estimate these highly uncertain values separately), which is even more directly relevant to the degree of TCR bias.  Indeed, if we look here, there appears to be excellent correlation:




Impressive, eh?   Now, I will mention that I believe things to be less pretty than the r^2=0.92 value shown above.  This is primarily because the 3 models bundled in Shindell et al (2014) actually have very different rates of global warming, as well as the NH/SH ratios, so I’m not sure it makes sense to bundle them, but have done so here for consistency with S14. 

One problem with using my method here and applying it to the“real-world”  is that we don’t know what the NH/SH warming ratio would be for the real world in the GHG-only scenario.  However, given the high value of 1.48 observed for the real-world “all-forcing” case, I suspect that the difference between this ratio and the GHG-only scenario can’t be that large, unless models have seriously underestimated the historicalGHG warming ratios.  Moreover, I would argue that the value is likely to be better constrained from models than the value of E, which depends on the much more uncertain aerosol properties.   Regardless, the average model warming ratio for NH/SH for the historicalGHG simulations in the above models is 1.54.  If you plug in the observed value of 1.48 for the “historical” observed NH/SH ratio in the real world, and use the linear regression from above, the estimated TCR bias amplification factor is 0.96.  This would suggest using the “simple” method slightly overestimates TCR, but by an extremely small fraction. 


While Shindell (2014) uses several GCM results to argue that traditional methods to calculate TCR lead to an underestimate, testing these methods against the outputs of those same GCMs seems to suggest the “simple” (E=1.0) methods perform better than the S14 “corrected” method.  Furthermore, when we consider the actual observed NH/SH warming ratio, it also seems to suggest that the TCR bias in the traditional/simple method is either very small or non-existent. 

Data and code.


  1. Very interesting. Shindell (2014) seems to suggest that the transient sensitivity of the NH is 60% greater than that of the SH. If so, then if NH/SH warming ratio is 1.48, that would seem to suggest that the effect of aerosol inhomogeneities is smaller than suggested by Shindell.

    Comment by andthentheresphysics — March 15, 2014 @ 3:06 pm

    • Thanks Anders. Yes, I think it likely means that the effect of aerosol inhomogeneities is smaller, or the aerosol forcing itself is smaller, since the approach can’t really distinguish between the two. It is also possible that the NH/SH warming ratio is higher under a WMGHG-only scenario than captured by models, although I think this less likely.

      Comment by troyca — March 17, 2014 @ 8:23 am

  2. […] 2014/03/15: TMasters: Does the Shindell (2014) apparent TCR estimate bias apply to the real world? […]

    Pingback by Another Week of Global Warming News, March 16, 2014 – A Few Things Ill Considered — March 17, 2014 @ 6:42 am

  3. Hi Troy,

    I don’t have access to the full Shindell paper so I can’t comment on most of your observations about model performance. I agree that Shindell’s hypothesis that aerosol forcing may have been more effective than that of ghgs is plausible, but remains unproven. I have one question regarding your comments. You state, “Obviously, if there was a large cooling effect from aerosols concentrated primarily in the NH (due to a large enhancement of the aerosol effect), we would expect to see more warming in the SH than the NH!” In fact, isn’t that what we see during the putative interval of greatest aerosol forcing increase shortly after World War 2, whereas the post-1980 interval during which ghg forcing outstripped aerosol forcing was characterized by greater NH warming – hemispheric warming? My sense at the moment is that Shindell’s hypothesis probably hasn’t been adequately justified by the evidence he presents, but may very well prove to be true, albeit to an unknown extent. That obviously can’t be offered as more than a tentative suggestion at this point.

    Comment by Fred Moolten — March 17, 2014 @ 1:01 pm

    • Fred, I encourage you to look at the supplement of Shindell that I have linked to, which does not require access and provides sufficient information to review my comments on the method’s performance on models (combined with the temperature data for those models that I have supplied). Regarding Shindell (2014), I think we need to be careful to consider what might be “right” about Shindell vs. what it implies about TCR sensitivity estimates:

      1) Do aerosol forcing disproportionally affect the NH relative to the SH? Of course! This is not controversial (or new) in Shindell, and both Isaac Held and my post here argue that the recent rate of NH / SH warming likely implies a lower than models TCR for this reason.

      2) Does this mean there is a different “enhancements” for these forcings relative to GHG due to the lower heat capacity of the land mass in the NH? Perhaps, although it is unclear on what time-scales this occurs and if there is a “correction” for this effect by a horizontal heat transfer, as well as if once “mixed” with GHG this somewhat affects the enhancement. As I show above, testing this method on the models does not indicate that the Shindell (2014) “correction” works, with a notable issue in the IPSL-CM5-LR case.

      But the real novel question that Shindell (2014) was to suppose to answer is #3:

      3) Does Shindell (2014) show that simple TCR estimates from real world temperature observations (such as that of Otto) between the base period (1860-1879) and the 2000s are substantially biased low, and does using the enhancement factor in Shindell produce a better estimate (1.7K)? The answer to this, in light of my analysis above, seems almost certainly to be “no”. The observed NH/SH ratio over that time period of 1.48 is completely inconsistent with models that show a bias in the TCR estimate, while being consistent with those models that shown almost no bias. Whether the explanation for this a lower “enhancement” when different forcings are mixed (vs. calculated separately), greater horizontal (hemispheric) heat transfer on longer timescales, smaller aerosol forcings in general, or something else, I don’t know.

      Comment by troyca — March 17, 2014 @ 3:30 pm

  4. I’ve written similar sentiments elsewhere. It’s clear the first test of the hypothesis – disproportionately slow warming of NH Ext – is not evident from surface temperature observations. However, having looked further into the details things become a little murkier. First, breaking up into land and sea areas the NH/SH warming ratio for SSTs is 0.85 or 1.0 from ERSST3v and HadSST3 respectively (clipped to 60ºS-60ºN, 1994-2013 vs. 1880-1899). The CMIP5 ensemble median for the same is 1.07, and one of the key predictors of a model lying above or below the median appears to be hemispherical asymmetry of aerosol forcing. For example the GFDL and HadGEM2 models with relatively large hemispherical forcing differences tended to produce NH/SH SST ratios of about 0.9-1.1. The IPSL model with a smaller hemispheric difference produced ratios of around 1.2, inconsistent with obs. The less-asymmetric CSIRO and CanESM2 models did produce ratios inline with observations but this appears to be partially caused by a naturally small NH/SH ratio according the WMGHG figures you’ve shown.

    So, in ocean areas there is a plausible case for an aerosol-induced NH/SH contrast. The overall observed NH/SH contrast is therefore due to very strong land warming in the Northern Hemisphere. This can also be seen in the NH land/sea warming contrast, which is about 2.0 – 3.0 depending on dataset combination. Using Berkeley land it’s 2.4 and 2.8 against HasSST3 and ERSSTv3 respectively. That compares to model average from historical+rcp45 runs being 1.5, 95% range 1.3-1.8. HistoricalGHG model runs give similar values for centennial length trends – 1.5 is a fairly standard number for land/sea warming contrast. Generally, land and ocean interact with and feed each other so the theory is that you’d need some kind of persistent factor to maintain a larger contrast.

    Based on these points it’s not clear to me that proposing either linearly scaled or less homogeneous aerosol forcing would, by itself, provide a satisfactory explanation for the detail of surface temperature observations: An explanation for the large land/sea warming contrast is needed. You can say a reasonable size NH/SH warming ratio due to WMGHG and a linearly scaled down aerosol forcing can explain the relatively large observed NH/SH ratio, but why only on land?

    One possible answer does come from aerosol forcing – Allen and Sherwood 2010 found that a modelled implementation of the semi-direct effect had very different effects on land areas versus ocean, tending to warm the land and cool the ocean. The problem I can see is that they determine this effect to be greatest in NH Summer, whereas the observed NH land/sea warming contrast is greatest in Winter and smallest in Summer.

    I think the key to this, and possibly a number of other puzzles, is understanding the causes of the observed NH land/sea warming contrast.

    Comment by Paul S — March 17, 2014 @ 4:51 pm

    • Thanks Paul, that is quite interesting. I was not aware that the NH land / sea warming ratio was that large, or the NH / SH SST warming that low. It is indeed a puzzle, and the idea that aerosols might relatively warm land (not sure what the relative effects are of DRE vs. semi-direct effect) while cooling ocean would certainly explain why the formula with am “enhancement” formula might not work, although, as you suggest, it seems that the high NH land/SST warming ratio may not so easily be explained. Do you know of any papers directly trying to address this contrast between models and observed temperatures?

      Comment by troyca — March 17, 2014 @ 7:53 pm

      • Thanks Troy,

        There is a rich literature on land/sea warming contrast as a phenomenon, but almost all of it is has been primarily concerned with understanding what GCMs are doing when producing a contrast. As far as I can see the observed NH land and sea trends have received very little published attention.

        Wallace et al. 2012 is the only one I can find which deals with a relevant issue directly. They suggest observed Winter land warming is mostly a facet of internal variability rather than forced response. Not sure what exactly that would mean for aerosol forcing. If NH land warming would have been small without this internal variability influence does that mean perhaps strong aerosol forcing has otherwise suppressed NH warming? Or maybe internal variability shifted the seasonality of land warming which may have happened anyway due to to the differential semi-direct effect noted by Allen and Sherwood.

        Comment by Paul S — March 18, 2014 @ 4:56 am

  5. Troy I’m curious, why only look at the entire record rather than focusing on time frames during which the aerosol forcing was greatest? For example, mid-century during intense industrial development, prior to passage of the Clean Air Act. During that time the aerosol forcing was large, global surface temps flattened, and that was primarily due to NH cooling while the SH continued to warm, IIRC. That would seem to support Shindell’s conclusions.

    I’m certainly in favor of examining all the data, but there are also other factors that can influence the NH/SH ratio besides aerosols.

    Comment by dana1981 — March 17, 2014 @ 10:12 pm

    • Hi Dana,

      Troy I’m curious, why only look at the entire record rather than focusing on time frames during which the aerosol forcing was greatest? For example, mid-century during intense industrial development, prior to passage of the Clean Air Act.

      I look at the entire record as that is the period for which Shindell (2014) provides data and performs his analysis, and it is over that period that the results of that paper claim Otto et al., (2013) results in an underestimate. Specifically,the values of E, as well as all forcing changes in tables 2 & 3 of the supplement, are all calculated over the entire period. While I agree that looking at other periods could be interesting academically, they wouldn’t really address the question of whether there exists a TCR bias in the Otto et al result that Shindell examines, nor would looking at these shed light on whether the Shindell method (over which the most likely value of 1.7K is calculated) is likely to be an overestimate.

      During that time the aerosol forcing was large, global surface temps flattened, and that was primarily due to NH cooling while the SH continued to warm, IIRC. That would seem to support Shindell’s conclusions.

      As with Fred above, I have to ask, which conclusions? If the idea is that on some timescales, it is possible that the forcing inhomogeneity could lead to different rates of warming in the NH vs. SH, I don’t think that is controversial (nor novel to Shindell). However, if we want to answer the question of whether this inhomogoneity is likely to lead to significant TCR bias in estimates using real-world observations from the base period (1860-1879) through 2000 (as Shindell argues), and whether a simply calculated value of E from models is going to improve such an estimate, I think the best interval to examine is the very one in question. And so in regards to *that* conclusion, I think there is little observational support for Shindell (2014).

      I agree that there are other factors that can influence the NH/SH ratio besides aerosols. However, I fear I may be parroting some of the Isaac Held post from my answer to Fred here, because most possibilities would seem to suggest a lower TCR. Consider two possibilities:

      1) If the higher-than-most NH/SH warming ratio 1.48 is *forced*, then it either means that a) the cooling influence of aerosols is smaller than suggested by Shindell, or that they are not as hemispherically imbalanced as suggested, or b) some other warming forcing (likely WMGHG) is creating disproportionately larger NH than SH warming than models calculate, in which case this forcing should have an “enhancement” factor for the same reasons that aerosols would have this factor.

      2) If this higher-than-most NH/SH warming ratio is *unforced* (horizontal heat transfer), then this transfer from the SH to NH would contribute to the global temperature rise (again, because of the lower heat capacity of the NH due to greater land mass argument).

      So yes, there are other options besides 1a), but they generally lead to a similar conclusion. Paul S brings up an interesting point above, which is that the NH/SH warming ratio is largely the result of a higher land heating rate, which is somewhat of a mystery. But it is rather obvious why this fact does not help Shindell (2014), which relies on the idea that aerosols have a larger enhancement due to their disproportionate cool influence on a greater land mass area in the NH (although it is an interesting puzzle to be solved in its own right).

      Comment by troyca — March 18, 2014 @ 12:16 am

  6. RealClimate has a post about Shindell (2014) here. The comment I just left is as follows:

    Did you test your updated method (for calculating TCR) on each model using the value of E calculated for that model? That would seem to be a good test of whether the method produces a good estimate of TCR independent of the uncertainty in E. I tried such a thing, and my main objection to the Shindell (2014) paper is that when I test the “simple” Otto method vs. the Shindell method on the same model set in the paper, the Otto et al (2013) method still seems to perform better. This is particularly true in the cases where the NH/SH warming ratio in that model is similar to the one observed. See for more details. For example, in the model with the largest value of E (IPSL-CM5-LR), the Shindell (2014) method overestimates the TCR by 40% (!) when using the actual output from that model, whereas using the simpler approach of E=1.0 (a la Otto et al. (2013)) only leads to a tiny underestimate of only 6%. Moreover, the NH/SH warming ratio in that model is 1.49, very close to that of the observed ratio of 1.48 in Cowtan and Way (2013). Given that the degree of under-estimation of TCR using the Otto method seems inversely correlated with the NH/SH warming ratio, at least in the models used in Shindell (2014), it would seem that the rather large NH/SH warming ratio observed in the “real” earth system indicates a tiny to non-existent underestimation of TCR when using those simple methods (e.g. Otto et al) in the real world. What do you think?

    Comment by troyca — April 8, 2014 @ 9:39 am

    • Drew Shindell responded with an informative reply here: I have posted a comment that has not appeared yet, but hopefully will soon:

      Hi Drew (#19),

      Thanks for the response. I agree that given that calculation, testing your method on those same models is not quite independent – it essentially just shows the sensitivity to the method of calculating E (whether using difference in decadal means vs. regression to calculate TCR, or using TCR_histGHG vs. TCR_1%CO2 in the denominator). That being said, I do include the land use forcing in my table, and I’d be surprised if the Strat_WV+solar+volcanic change in forcing is much larger than 0.1 W/m^2 (given that the latter offsets some of the former two), so the discrepancies are likely due to the different values of dT calculated in histAll. I calculated mine using a weighted average from the runs available at the ETH subarchive (which should be the same as CMIP5, but is easier IMO to access), so it is possible that I’ve made an error…would it be possible for you to give the values you get for dT_histAll for those various models?

      “Hence the NH/SH ratio does not make a good quantitative test of modeled sensitivity in my opinion”.

      I want to stress that I am saying the NH/SH ratio is a quantitative test of the bias produced by using the simpler (E=1.0) method, and that used in conjuction with the Otto et al., (2013) method this would likely constrain the TCR better. Consider that the bias in the E=1.0 scenario is going to be a function of the actual value of E and the magnitude of F_inhom, with the former value depending largely on the difference in hemispheric forcing. Given that, it is hard to reconcile a large magnitude combined value of E and F_inhom with a large NH/SH ratio (for example, IPSL has a large E but a lower magnitude F_inhom, which allows for the larger NH/SH ratio) So ideally one would assign lower probability to the large E, very negative F_inhom events given the observed ratio, but a Monte Carlo sampling that treats the uncertainties in these values as independent would not do so. You mentioned that the rate of SH warming may be sensitive to hemispheric heat transfer, but in that case couldn’t one argue that the heat transfered from one hemisphere to another would have a differential effect in surface warming, for many of the same reasons (e.g. greater land mass in the NH) that the F_inhom forcings have a different effect? I would suggest that the degree to which horizontal heat tranfer is reflected in the NH/SH ratio, it would also impact the TCR bias, leaving the ratio as a good constraint on the potential bias.

      I think Tim’s point (#21) about examining the combination of forcings and natural variablity that could produce this effect is interesting, and would be important for constraining TCR. Paul S also noted that much of the NH/SH ratio comes from the greater land / ocean warming ratio in the NH than is generally modelled, which is another mystery. And while there may be uncertainty regarding the NH/SH ratio of 1.48 from pre-indust to now, I think there is considerably less uncertainty about (for instance) this ratio from ~1975 to present, which is quite large relative to models. Similarly (and perhaps relatedly), the magnitude of the change in aerosol forcing from ~1975 to present relative to the change in all forcings is much smaller than from pre-ind through present, which I think should make the TCR estimated over that period insensitive to the value of E. As such, it seems to me (and Nic Lewis also brought up this point in his CA post) that if there were a substantial bias in the E=1.0 method, the Otto et al estimate from 1970s-2000s should be substantially larger than the base-period through 2000s estimate, right?

      Comment by troyca — April 11, 2014 @ 1:20 pm

  7. Troy, Thank you for an excellent piece of analytical work, which I missed when you posted it.

    There are certainly puzzles in the behaviour of the real world here. But your conclusion that the Shindell method overestimates TCR seems pretty convincing to me. It meshes well with two points I made in my Climate Audit post, that:

    a) if TCR were pretty much in line with the CMIP5 model average as Shindell claims, then modelled warming in the period since 1979 – during which time aerosol forcing is thought to have changed little – should have been close to actual warming, not ~50% faster; and

    b) the multimodel detection and attribution results cited in AR5 suggest, for both 1861-2010 and 1951-2010, models respond by wildly varying amounts to anthropogenic non-GHG (mainly aerosol) forcing, and on average by nearly 70% too much. Even worse, the four out of six of Shindell’s models that are included in the AR5 D&A analysis have scaling factors that are inconsistent with unity. This point indicates to me that the CMIP5 models Shindell uses cannot be regarded as providing reliable information about, in particular, how the real world responds to aerosols, implying Shindell’s model based analysis to be of questionable value.

    It is notable that Shindell entirely failed to address either of the above two points in his post today at RealClimate. (For the record, I also take issue with a number of things he did write there about my Climate Audit post.)

    Regarding your point about natural forcings being omitted by Shindell, the same point worried me. In fact, he does include solar forcing as part of GHG forcing (along with stratospheric water vapour and contrails), although I can’t quite agree his figures to AR5 estimates. Including the omitted volcanic forcing would seem to make Shindell’s TCR estimates even higher relative to the actual model TCRs.

    Comment by niclewis — April 8, 2014 @ 3:01 pm

    • Thanks Nic. Drew Shindell ( pointed out that since T_inhom is calculated by subtracting T_GHG and T_nat from T_hist, his method (provided E is calculated using decadal mean differences and using histGHG in the denominator) should actually produce the correct estimate on the respective model just by the nature of its circularity. I agree with this, but it raises the question of how we then ended up with such different values of dT. I think the more salient point is that the bias mentioned in Shindell (2014) for the Otto et al., (2013) method seems to show up in models with the smaller values of NH/SH ratios, which makes sense intuitively, and suggests a likely small-to-non-existent bias based on the larger observed NH/SH. This likely ties into your point #b about the aerosol response. Regarding point #a, I mentioned a similar idea (but specifically regarding the Otto 1970s-2000s TCR estimate) in my latest comment at RC that should hopefully be up soon.

      Comment by troyca — April 11, 2014 @ 1:32 pm

  8. […] should add, however, that Troy Masters did an analysis that suggested that the inhomogeneity correction may not be as large as suggested by Shindell. I […]

    Pingback by Krummer & Dessler on Climate Sensitivity | And Then There's Physics — May 9, 2014 @ 1:28 am

  9. […] to be other issues in the actual energy balance calculations for the mean time).  While I have noted previously that the spatial warming pattern appears to indicate a value of “E” (the Shindell […]

    Pingback by On forcing enhancement, efficacy, and Kummer and Dessler (2014) | Troy's Scratchpad — May 9, 2014 @ 10:42 pm


    why fall into the disillusionist trap , stick to basic physics.

    Comment by Rogueelement451 — May 20, 2014 @ 8:08 am

  11. […] you will recall, one of my criticisms of Shindell et al (2014) was that it did not consider the observed spatial warming pattern.  […]

    Pingback by Estimating ECS bias from local feedbacks and observed warming patterns–example with GFDL-CM3 | Troy's Scratchpad — June 13, 2014 @ 10:45 pm

RSS feed for comments on this post. TrackBack URI

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

Create a free website or blog at

%d bloggers like this: